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All Journal Journal of Emerging Science and Engineering International Journal of Renewable Energy Development
Paramasivam, Prabhu
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Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Environmental Science

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Unlocking renewable energy potential: Harnessing machine learning and intelligent algorithms Le, Thanh Tuan; Paramasivam, Prabhu; Adril, Elvis; Nguyen, Van Quy; Le, Minh Xuan; Duong, Minh Thai; Le, Huu Cuong; Nguyen, Anh Quan
International Journal of Renewable Energy Development Vol 13, No 4 (2024): July 2024
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2024.60387

Abstract

This review article examines the revolutionary possibilities of machine learning (ML) and intelligent algorithms for enabling renewable energy, with an emphasis on the energy domains of solar, wind, biofuel, and biomass. Critical problems such as data variability, system inefficiencies, and predictive maintenance are addressed by the integration of ML in renewable energy systems. Machine learning improves solar irradiance prediction accuracy and maximizes photovoltaic system performance in the solar energy sector. ML algorithms help to generate electricity more reliably by enhancing wind speed forecasts and wind turbine efficiency. ML improves the efficiency of biofuel production by optimizing feedstock selection, process parameters, and yield forecasts. Similarly, ML models in biomass energy provide effective thermal conversion procedures and real-time process management, guaranteeing increased energy production and operational stability. Even with the enormous advantages, problems such as data quality, interpretability of the models, computing requirements, and integration with current systems still remain. Resolving these issues calls for interdisciplinary cooperation, developments in computer technology, and encouraging legislative frameworks. This study emphasizes the vital role of ML in promoting sustainable and efficient renewable energy systems by giving a thorough review of present ML applications in renewable energy, highlighting continuing problems, and outlining future prospects
Nanotechnology-based biodiesel: A comprehensive review on production, and utilization in diesel engine as a substitute of diesel fuel Le, Thanh Tuan; Tran, Minh Ho; Nguyen, Quang Chien; Le, Huu Cuong; Nguyen, Van Quy; Cao, Dao Nam; Paramasivam, Prabhu
International Journal of Renewable Energy Development Vol 13, No 3 (2024): May 2024
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2024.60126

Abstract

As a sustainable replacement for fossil fuels, biodiesel is a game-changer in the energy sector. There is no strategy to minimize biodiesel's significance as a sustainable, clean fuel source in light of the increasing climate change and environmental sustainability concerns. Nevertheless, conventional biodiesel production methods often run into problems like inadequate conversion efficiency and inappropriate fuel properties, which impede their broad adoption. The revolutionary potential of nanotechnology to circumvent these limitations and revolutionize biodiesel consumption and production is explored in this review paper. There are new possibilities for improving biodiesel output and engine efficiency, thanks to nanotechnology, which can alter matter at the atomic and molecular levels. Using nano-catalysts, nano-emulsification processes, and nano-encapsulation procedures, researchers have made significant advances in improving biodiesel qualities such as stability, combustion efficiency, and viscosity. Through a comprehensive analysis of current literature and research data, this article elucidates the crucial role of nanotechnology in advancing biodiesel technology. By shedding light on the most current advancements, challenges, and potential future outcomes in nano-based biodiesel manufacturing and consumption, this review hopes to add to the growing corpus of knowledge in the field and inspire additional innovation. In conclusion, there is great hope for a sustainable energy future, increased economic growth, and reduced environmental impacts through the application of nanotechnology.  
Soft computing-based modelling and optimization of NOx emission from a variable compression ratio diesel engine Paramasivam, Prabhu; Naima, Khatir; Dzida, Marek
Journal of Emerging Science and Engineering Vol. 2 No. 2 (2024)
Publisher : BIORE Scientia Academy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/jese.2024.e21

Abstract

Machine learning method and statistical method used for model prediction and optimization of third generation biodiesel-diesel blend powered variable compression engine High R2 values of 0.9998 and 0.9994 were observed in the training and testing phase of the model, respectively, indicating that The results confirm the robustness of the forecasting system. It was shown that the model accuracy means squared errors remained low at 0.0002 and 0.0014. These results were then confirmed by desirability-based optimization, which succeeded in achieving the values of the set parameters It should be noted that the compression ratio (CR), fuel injection pressure, and engine load were optimized to meet the defined parameters, resulting in a NOx emissions reduction as 222.8 ppm. The research illustrates the efficacy of desirability-based optimization in attaining targeted performance targets across important engine parameters whilst also reducing the impact on the environment.
Application of response surface methodology to optimize the dual-fuel engine running on producer gas Nguyen, Phuoc Quy Phong; Tran, Viet Dung; Nguyen, Du; Luong, Cong Nho; Paramasivam, Prabhu
International Journal of Renewable Energy Development Vol 14, No 2 (2025): March 2025
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2025.60927

Abstract

This work develops a computational framework that optimizes the performance and emissions of a dual-fuel diesel engine running on biomass-derived producer gas as the main fuel and diesel as the pilot fuel. The study connects essential responses, brake thermal efficiency, peak combustion pressure, and emissions of nitrogen oxides (NOx), carbon monoxide (CO), and unburnt hydrocarbon (HC) with controllable factors like engine load and pilot fuel injection duration. The approach consists of simulating the impacts of these controllable inputs on engine performance, then optimization to find the optimal fuel injection pressure to balance performance and emissions. The results show that engine load considerably affects NOx emissions and brake thermal efficiency; greater loads lower CO emissions but raise HC emissions at low compression ratios. Although it had little effect on NOx emissions, fuel injection pressure was vital in balancing general engine performance. Using optimization, an optimal fuel injection pressure value of 218.5 bar was identified, thereby producing a brake thermal efficiency of 27.35% and lowering emissions to 80 ppm HC, 202 ppm NOx, and 92 ppm CO. This computational method offers a strategic means for improving the efficiency of dual-fuel engines while reducing their environmental impact, hence guiding more sustainable and effective engine operation.
Co-Authors Cao, Dao Nam Duong, Minh Thai Dzida, Marek Elvis Adril Le, Huu Cuong Le, Minh Xuan Le, Thanh Tuan Luong, Cong Nho Naima, Khatir Nguyen, Anh Quan Nguyen, Du Nguyen, Phuoc Quy Phong Nguyen, Quang Chien Nguyen, Van Quy Tran, Minh Ho Tran, Viet Dung